37_rmok_starter.ipynb

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     "text": [
      "D:\\Anaconda\\envs\\neuralforecast-env\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from datasetsforecast.long_horizon import LongHorizon\n",
    "\n",
    "from neuralforecast.core import NeuralForecast\n",
    "from neuralforecast.models import NHITS, RMoK, TSMixer\n",
    "\n",
    "from utilsforecast.losses import mae, mse\n",
    "from utilsforecast.evaluation import evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "77aa7736-8e70-4f5a-af06-34d842141cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(name):\n",
    "    if name == \"ettm1\":\n",
    "        Y_df, *_ = LongHorizon.load(directory='./', group='ETTm1')\n",
    "        Y_df['ds'] = pd.to_datetime(Y_df['ds'])\n",
    "        val_size = 11520\n",
    "        test_size = 11520\n",
    "        freq = '15T'\n",
    "    elif name == \"ettm2\":\n",
    "        Y_df, *_ = LongHorizon.load(directory='./', group='ETTm2')\n",
    "        Y_df['ds'] = pd.to_datetime(Y_df['ds'])\n",
    "        val_size = 11520\n",
    "        test_size = 11520\n",
    "        freq = '15T'\n",
    "    elif name == 'etth1':\n",
    "        Y_df, *_ = LongHorizon.load(directory='./', group='ETTh1')\n",
    "        Y_df['ds'] = pd.to_datetime(Y_df['ds'])\n",
    "        val_size = 2880\n",
    "        test_size = 2880\n",
    "        freq = 'H'\n",
    "    elif name == \"etth2\":\n",
    "        Y_df, *_ = LongHorizon.load(directory='./', group='ETTh2')\n",
    "        Y_df['ds'] = pd.to_datetime(Y_df['ds'])\n",
    "        val_size = 2880\n",
    "        test_size = 2880\n",
    "        freq = 'H'\n",
    "\n",
    "    return Y_df, val_size, test_size, freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b98e9c4d-54aa-46ec-85bf-87d13bc9e948",
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      "INFO:lightning_fabric.utilities.seed:Seed set to 1\n",
      "INFO:lightning_fabric.utilities.seed:Seed set to 1\n",
      "INFO:lightning_fabric.utilities.seed:Seed set to 1\n",
      "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (mps), used: True\n",
      "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
      "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
      "INFO:pytorch_lightning.callbacks.model_summary:\n",
      "  | Name    | Type          | Params | Mode \n",
      "--------------------------------------------------\n",
      "0 | loss    | MAE           | 0      | train\n",
      "1 | padder  | ConstantPad1d | 0      | train\n",
      "2 | scaler  | TemporalNorm  | 0      | train\n",
      "3 | dropout | Dropout       | 0      | train\n",
      "4 | experts | ModuleList    | 138 K  | train\n",
      "5 | gate    | Linear        | 388    | train\n",
      "6 | softmax | Softmax       | 0      | train\n",
      "7 | rev     | RevIN         | 14     | train\n",
      "--------------------------------------------------\n",
      "139 K     Trainable params\n",
      "0         Non-trainable params\n",
      "139 K     Total params\n",
      "0.556     Total estimated model params size (MB)\n",
      "14        Modules in train mode\n",
      "0         Modules in eval mode\n"
     ]
    },
    {
     "name": "stdout",
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     "text": [
      "Epoch 999: 100%|██████████| 1/1 [00:07<00:00,  0.14it/s, v_num=148, train_loss_step=0.340, train_loss_epoch=0.340, valid_loss=0.402]"
     ]
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     "text": [
      "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_steps=1000` reached.\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 999: 100%|██████████| 1/1 [00:07<00:00,  0.14it/s, v_num=148, train_loss_step=0.340, train_loss_epoch=0.340, valid_loss=0.402]"
     ]
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     "text": [
      "INFO:pytorch_lightning.utilities.rank_zero:Trainer already configured with model summary callbacks: [<class 'pytorch_lightning.callbacks.model_summary.ModelSummary'>]. Skipping setting a default `ModelSummary` callback.\n"
     ]
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     "name": "stdout",
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      "\n"
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      "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (mps), used: True\n",
      "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
      "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:15<00:00,  0.06it/s]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (mps), used: True\n",
      "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
      "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
      "INFO:pytorch_lightning.callbacks.model_summary:\n",
      "  | Name          | Type                     | Params | Mode \n",
      "-------------------------------------------------------------------\n",
      "0 | loss          | MAE                      | 0      | train\n",
      "1 | padder        | ConstantPad1d            | 0      | train\n",
      "2 | scaler        | TemporalNorm             | 0      | train\n",
      "3 | norm          | ReversibleInstanceNorm1d | 14     | train\n",
      "4 | mixing_layers | Sequential               | 184 K  | train\n",
      "5 | out           | Linear                   | 27.7 K | train\n",
      "-------------------------------------------------------------------\n",
      "212 K     Trainable params\n",
      "0         Non-trainable params\n",
      "212 K     Total params\n",
      "0.849     Total estimated model params size (MB)\n",
      "28        Modules in train mode\n",
      "0         Modules in eval mode\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 999: 100%|██████████| 1/1 [00:00<00:00,  2.37it/s, v_num=150, train_loss_step=0.368, train_loss_epoch=0.368, valid_loss=0.402]"
     ]
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     "text": [
      "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_steps=1000` reached.\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 999: 100%|██████████| 1/1 [00:00<00:00,  2.36it/s, v_num=150, train_loss_step=0.368, train_loss_epoch=0.368, valid_loss=0.402]"
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     "text": [
      "INFO:pytorch_lightning.utilities.rank_zero:Trainer already configured with model summary callbacks: [<class 'pytorch_lightning.callbacks.model_summary.ModelSummary'>]. Skipping setting a default `ModelSummary` callback.\n",
      "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (mps), used: True\n",
      "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
      "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00,  4.79it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (mps), used: True\n",
      "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
      "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
      "INFO:pytorch_lightning.callbacks.model_summary:\n",
      "  | Name         | Type          | Params | Mode \n",
      "-------------------------------------------------------\n",
      "0 | loss         | MAE           | 0      | train\n",
      "1 | padder_train | ConstantPad1d | 0      | train\n",
      "2 | scaler       | TemporalNorm  | 0      | train\n",
      "3 | blocks       | ModuleList    | 3.2 M  | train\n",
      "-------------------------------------------------------\n",
      "3.2 M     Trainable params\n",
      "0         Non-trainable params\n",
      "3.2 M     Total params\n",
      "12.759    Total estimated model params size (MB)\n",
      "34        Modules in train mode\n",
      "0         Modules in eval mode\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Sanity Checking DataLoader 0:   0%|          | 0/1 [00:00<?, ?it/s]"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/marcopeix/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/torch/nn/functional.py:4024: UserWarning: The operator 'aten::upsample_linear1d.out' is not currently supported on the MPS backend and will fall back to run on the CPU. This may have performance implications. (Triggered internally at /Users/runner/work/_temp/anaconda/conda-bld/pytorch_1711403226260/work/aten/src/ATen/mps/MPSFallback.mm:13.)\n",
      "  return torch._C._nn.upsample_linear1d(input, output_size, align_corners, scale_factors)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 499: 100%|██████████| 1/1 [00:00<00:00,  1.72it/s, v_num=152, train_loss_step=0.273, train_loss_epoch=0.273, valid_loss=0.419]"
     ]
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    {
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     "text": [
      "INFO:pytorch_lightning.utilities.rank_zero:Trainer already configured with model summary callbacks: [<class 'pytorch_lightning.callbacks.model_summary.ModelSummary'>]. Skipping setting a default `ModelSummary` callback.\n",
      "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (mps), used: True\n",
      "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
      "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00,  2.63it/s]\n"
     ]
    }
   ],
   "source": [
    "Y_df, val_size, test_size, freq = load_data('ettm1')\n",
    "horizon = 96\n",
    "\n",
    "models = [\n",
    "    # RMoK\n",
    "    RMoK(\n",
    "        h=horizon,\n",
    "        input_size=horizon,\n",
    "        n_series=7,\n",
    "        taylor_order=3,\n",
    "        jacobi_degree=6,\n",
    "        wavelet_function='mexican_hat',\n",
    "        dropout=0.1,\n",
    "        revine_affine=True,\n",
    "        learning_rate=0.001,\n",
    "        scaler_type='identity',\n",
    "        max_steps=1000,\n",
    "        early_stop_patience_steps=3),\n",
    "    )\n",
    "    TSMixer(h=horizon, input_size=3*horizon, n_series=7, max_steps=1000, early_stop_patience_steps=3),\n",
    "    NHITS(h=horizon, input_size=3*horizon, max_steps=1000, early_stop_patience_steps=3),\n",
    "]\n",
    "\n",
    "nf = NeuralForecast(models=models, freq=freq)\n",
    "nf_preds = nf.cross_validation(df=Y_df, val_size=val_size, test_size=test_size, n_windows=None)\n",
    "nf_preds = nf_preds.reset_index()\n",
    "\n",
    "ettm1_evaluation = evaluate(df=nf_preds, metrics=[mae, mse], models=['RMoK', 'TSMixer', 'NHITS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "372ded76-3954-432f-995d-2c4e44dd747e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>RMoK</th>\n",
       "      <th>TSMixer</th>\n",
       "      <th>NHITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mae</td>\n",
       "      <td>0.355421</td>\n",
       "      <td>0.333564</td>\n",
       "      <td>0.356006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mse</td>\n",
       "      <td>0.332293</td>\n",
       "      <td>0.290767</td>\n",
       "      <td>0.342725</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "  metric      RMoK   TSMixer     NHITS\n",
       "0    mae  0.355421  0.333564  0.356006\n",
       "1    mse  0.332293  0.290767  0.342725"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ettm1_evaluation = ettm1_evaluation.drop(['unique_id'], axis=1)\\\n",
    "                                   .groupby('metric')\\\n",
    "                                   .mean()\\\n",
    "                                   .reset_index()\n",
    "\n",
    "ettm1_evaluation"
   ]
  },
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   "id": "bbb98245-bef0-4ff2-8bd1-7c18a3aa1f12",
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